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 "cells": [
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Hello, I'm ***IPython-Dashboar***, which is inspired by one of the greatest packages, ***IPython***.\n",
    "\n",
    "I'm a stand alone, light-weight web server for building, sharing graphs created in ipython. Aiming at building for data science, data analysis guys. Building an interactive visualization, collaborated dashboard, and real-time streaming graph.\n",
    "\n",
    "If you are doing data exploring in IPython notebook, and draw some graph or select some metrics from database, which will be used to present to others, but you don't like to share the code or the complicated logic [ for private or page cleaning ], I think I can help you to do the present part job.\n",
    "\n",
    "In this tutorial, I'll show you how to do data exploring in ipython notebook and how to share it to others without ipython.\n",
    "\n",
    "![usage](usage-1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load packages [ One can't make bricks without straw ]\n",
    "\n",
    "\n",
    "- I recommend import package in a readable and reasonable order, which will be useful as the project gets larger.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# built-in package\n",
    "import os\n",
    "import sys\n",
    "import json\n",
    "import time\n",
    "import datetime as dt\n",
    "\n",
    "# third-parth package\n",
    "import dashboard as dash\n",
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import seaborn\n",
    "import mpld3\n",
    "\n",
    "# package configre\n",
    "pd.options.display.max_columns = 100\n",
    "pd.options.display.max_rows = 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load data \n",
    "\n",
    "- I've prepared a test data on this repo.\n",
    "- Don't forget to take a look at the data before exploring it, that's a good habit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = \"\"\"https://github.com/litaotao/IPython-Dashboard/raw/v-0.1.2-visualiza-table/docs/people_number_by_province_lateset_10_years.csv\"\"\"\n",
    "data = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地区</th>\n",
       "      <th>2014年</th>\n",
       "      <th>2013年</th>\n",
       "      <th>2012年</th>\n",
       "      <th>2011年</th>\n",
       "      <th>2010年</th>\n",
       "      <th>2009年</th>\n",
       "      <th>2008年</th>\n",
       "      <th>2007年</th>\n",
       "      <th>2006年</th>\n",
       "      <th>2005年</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>2152</td>\n",
       "      <td>2115</td>\n",
       "      <td>2069</td>\n",
       "      <td>2019</td>\n",
       "      <td>1962</td>\n",
       "      <td>1860</td>\n",
       "      <td>1771</td>\n",
       "      <td>1676</td>\n",
       "      <td>1601</td>\n",
       "      <td>1538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>1517</td>\n",
       "      <td>1472</td>\n",
       "      <td>1413</td>\n",
       "      <td>1355</td>\n",
       "      <td>1299</td>\n",
       "      <td>1228</td>\n",
       "      <td>1176</td>\n",
       "      <td>1115</td>\n",
       "      <td>1075</td>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>河北省</td>\n",
       "      <td>7384</td>\n",
       "      <td>7333</td>\n",
       "      <td>7288</td>\n",
       "      <td>7241</td>\n",
       "      <td>7194</td>\n",
       "      <td>7034</td>\n",
       "      <td>6989</td>\n",
       "      <td>6943</td>\n",
       "      <td>6898</td>\n",
       "      <td>6851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    地区  2014年  2013年  2012年  2011年  2010年  2009年  2008年  2007年  2006年  2005年\n",
       "0  北京市   2152   2115   2069   2019   1962   1860   1771   1676   1601   1538\n",
       "1  天津市   1517   1472   1413   1355   1299   1228   1176   1115   1075   1043\n",
       "2  河北省   7384   7333   7288   7241   7194   7034   6989   6943   6898   6851"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Traditionaly way of plotting [ I really like ipython indeed, but ... ]\n",
    "\n",
    "- matplotlib + seaborn + mpld3 : I really like it very much, but ...\n",
    "- raw html : ok, it a way indeed, but ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el365545289334562578749862\", {\"axes\": [{\"xlim\": [-0.5, 30.5], \"yscale\": \"linear\", \"axesbg\": \"#EAEAF2\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#262626\", \"text\": \"\\u5730\\u533a\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 11.0, \"position\": [0.5, -0.20416666666666669], \"rotation\": -0.0, \"id\": \"el36554528267984\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#262626\", \"text\": \"2014\\u5e74\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [0.94119623655913975, 0.94375000000000009], \"rotation\": -0.0, \"id\": \"el36554593827216\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#262626\", \"text\": \"2013\\u5e74\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [0.94119623655913975, 0.89513888888888893], 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\"data08\": [[6.75, 0.0], [7.0, 0.0], [7.0, 3833.0], [6.75, 3833.0]], \"data09\": [[7.75, 0.0], [8.0, 0.0], [8.0, 2426.0], [7.75, 2426.0]], \"data20\": [[18.75, 0.0], [19.0, 0.0], [19.0, 4754.0], [18.75, 4754.0]], \"data21\": [[19.75, 0.0], [20.0, 0.0], [20.0, 903.0], [19.75, 903.0]], \"data22\": [[20.75, 0.0], [21.0, 0.0], [21.0, 2991.0], [20.75, 2991.0]], \"data05\": [[3.75, 0.0], [4.0, 0.0], [4.0, 2505.0], [3.75, 2505.0]], \"data02\": [[0.75, 0.0], [1.0, 0.0], [1.0, 1517.0], [0.75, 1517.0]], \"data04\": [[2.75, 0.0], [3.0, 0.0], [3.0, 3648.0], [2.75, 3648.0]], \"data26\": [[24.75, 0.0], [25.0, 0.0], [25.0, 318.0], [24.75, 318.0]], \"data01\": [[-0.25, 0.0], [0.0, 0.0], [0.0, 2152.0], [-0.25, 2152.0]], \"data55\": [[23.0, 0.0], [23.25, 0.0], [23.25, 3502.0], [23.0, 3502.0]], \"data60\": [[28.0, 0.0], [28.25, 0.0], [28.25, 578.0], [28.0, 578.0]], \"data23\": [[21.75, 0.0], [22.0, 0.0], [22.0, 8140.0], [21.75, 8140.0]], \"data62\": [[30.0, 0.0], [30.25, 0.0], [30.25, 2264.0], [30.0, 2264.0]], \"data57\": [[25.0, 0.0], [25.25, 0.0], [25.25, 312.0], [25.0, 312.0]], \"data51\": [[19.0, 0.0], [19.25, 0.0], [19.25, 4719.0], [19.0, 4719.0]], \"data24\": [[22.75, 0.0], [23.0, 0.0], [23.0, 3508.0], [22.75, 3508.0]], \"data56\": [[24.0, 0.0], [24.25, 0.0], [24.25, 4687.0], [24.0, 4687.0]], \"data03\": [[1.75, 0.0], [2.0, 0.0], [2.0, 7384.0], [1.75, 7384.0]], \"data27\": [[25.75, 0.0], [26.0, 0.0], [26.0, 3775.0], [25.75, 3775.0]], \"data07\": [[5.75, 0.0], [6.0, 0.0], [6.0, 2752.0], [5.75, 2752.0]], \"data54\": [[22.0, 0.0], [22.25, 0.0], [22.25, 8107.0], [22.0, 8107.0]], \"data37\": [[5.0, 0.0], [5.25, 0.0], [5.25, 4390.0], [5.0, 4390.0]], \"data36\": [[4.0, 0.0], [4.25, 0.0], [4.25, 2498.0], [4.0, 2498.0]], \"data35\": [[3.0, 0.0], [3.25, 0.0], [3.25, 3630.0], [3.0, 3630.0]], \"data34\": [[2.0, 0.0], [2.25, 0.0], [2.25, 7333.0], [2.0, 7333.0]], \"data25\": [[23.75, 0.0], [24.0, 0.0], [24.0, 4714.0], [23.75, 4714.0]], \"data32\": [[0.0, 0.0], [0.25, 0.0], [0.25, 2115.0], [0.0, 2115.0]], \"data31\": [[29.75, 0.0], [30.0, 0.0], [30.0, 2298.0], [29.75, 2298.0]], \"data30\": [[28.75, 0.0], [29.0, 0.0], [29.0, 662.0], [28.75, 662.0]], \"data59\": [[27.0, 0.0], [27.25, 0.0], [27.25, 2582.0], [27.0, 2582.0]], \"data58\": [[26.0, 0.0], [26.25, 0.0], [26.25, 3764.0], [26.0, 3764.0]], \"data39\": [[7.0, 0.0], [7.25, 0.0], [7.25, 3835.0], [7.0, 3835.0]], \"data38\": [[6.0, 0.0], [6.25, 0.0], [6.25, 2751.0], [6.0, 2751.0]], \"data19\": [[17.75, 0.0], [18.0, 0.0], [18.0, 10724.0], [17.75, 10724.0]], \"data18\": [[16.75, 0.0], [17.0, 0.0], [17.0, 6737.0], [16.75, 6737.0]], \"data15\": [[13.75, 0.0], [14.0, 0.0], [14.0, 9789.0], [13.75, 9789.0]], \"data14\": [[12.75, 0.0], [13.0, 0.0], [13.0, 4542.0], [12.75, 4542.0]], \"data17\": [[15.75, 0.0], [16.0, 0.0], [16.0, 5816.0], [15.75, 5816.0]], \"data16\": [[14.75, 0.0], [15.0, 0.0], [15.0, 9436.0], [14.75, 9436.0]], \"data11\": [[9.75, 0.0], [10.0, 0.0], [10.0, 5508.0], [9.75, 5508.0]], \"data10\": [[8.75, 0.0], [9.0, 0.0], [9.0, 7960.0], [8.75, 7960.0]], \"data13\": [[11.75, 0.0], [12.0, 0.0], [12.0, 3806.0], [11.75, 3806.0]], \"data12\": [[10.75, 0.0], [11.0, 0.0], [11.0, 6083.0], [10.75, 6083.0]], \"data29\": [[27.75, 0.0], [28.0, 0.0], [28.0, 583.0], [27.75, 583.0]], \"data33\": [[1.0, 0.0], [1.25, 0.0], [1.25, 1472.0], [1.0, 1472.0]]}, \"id\": \"el36554528933456\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el365545289334562578749862\", {\"axes\": [{\"xlim\": [-0.5, 30.5], \"yscale\": \"linear\", \"axesbg\": \"#EAEAF2\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#262626\", \"text\": \"\\u5730\\u533a\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 11.0, \"position\": [0.5, -0.20416666666666669], \"rotation\": -0.0, \"id\": \"el36554528267984\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#262626\", \"text\": \"2014\\u5e74\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [0.94119623655913975, 0.94375000000000009], \"rotation\": -0.0, \"id\": \"el36554593827216\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#262626\", \"text\": \"2013\\u5e74\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 10.0, \"position\": [0.94119623655913975, 0.89513888888888893], \"rotation\": -0.0, \"id\": \"el36554593874128\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-0.5, 30.5], \"ylim\": [0.0, 12000.0], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#4C72B0\", \"edgewidth\": 0.3, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el36554564547536\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#4C72B0\", \"edgewidth\": 0.3, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el36554564549072\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#4C72B0\", \"edgewidth\": 0.3, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el36554564644944\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#4C72B0\", \"edgewidth\": 0.3, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el36554564646608\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#4C72B0\", \"edgewidth\": 0.3, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el36554564647888\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#4C72B0\", \"edgewidth\": 0.3, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el36554564703248\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#4C72B0\", \"edgewidth\": 0.3, 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.plot(x=\"地区\", y=[\"2014年\", \"2013年\"], kind=\"bar\", figsize=(12, 5))\n",
    "mpld3.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Not enough even arm matplotlib with seaborn, mpld3\n",
    "\n",
    "- If you just wanna share this graph above to others ?\n",
    "- if you wanna hidden/show a specific field, eg, just show one years' population ?\n",
    "- If you wanna know the exact number of a bar when the hovering on the bar ?\n",
    "- if ...\n",
    "- if ...\n",
    "\n",
    "![are-you-crazy](are-you-crazy.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. How IPython-Dashboard make it simple\n",
    "\n",
    "- No need to code complicated graph settings\n",
    "- Flexible to define\n",
    "- Able to share one graph, multi-graph in a dashboard\n",
    "- More ? coming soon ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Firstly, send you data to the IPython-Dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dash.client.sender(data, key='chinese_population', force=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 5.2 Secondly, oh, there is no second step, you can explore your graph, dashboard as long as opening [browser](http://127.0.0.1:9090)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![have-fun](have-fun.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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